5 citations · 16 across the 12 of their papers we have counts for
13 papers
Fast Random Compilers for Time-Dependent and Time-Independent Lindbladian Simulation
Youngjin Seo, Dhrumil Patel, Hyukjoon Kwon +1
Randomized product formulas provide simple and flexible methods for quantum simulation. While qDRIFT-type randomized simulation has been extended to Lindbladian dynamics, higher-or…
A Near-Optimal Joint Lower Bound for Sparse Quantum Linear System Solvers
Dhrumil Patel
Quantum linear system solvers form one of the central algorithmic primitives in quantum computing, with applications ranging from differential equations and optimization to machine…
Semidefinite optimization as many-body thermodynamics: Boltzmann, Fermi-Dirac, and Bose-Einstein frameworks
Michele Minervini, Nana Liu, Dhrumil Patel +1
We study the semidefinite programs (SDPs) of quantum information whose variables are density operators, measurement operators, or unbounded positive semidefinite operators, togethe…
Improved sample complexity bound for sample-based Lindbladian simulation
Siheon Park, Youngjin Seo, Byeongseon Go +3
We establish improved sample-complexity bounds for sample-based Lindbladian simulation based on the Wave Matrix Lindbladization (WML) algorithm. For a jump operator with dimens…
Quantum thermodynamics and semi-definite optimization
Nana Liu, Michele Minervini, Dhrumil Patel +1
In quantum thermodynamics, a system is described by a Hamiltonian and a list of non-commuting charges representing conserved quantities like particle number or electric charge, and…
Quantum natural gradient with thermal-state initialization
Michele Minervini, Dhrumil Patel, Mark M. Wilde
Parameterized quantum circuits (PQCs) are central to variational quantum algorithms (VQAs), yet their performance is hindered by complex loss landscapes that make their trainabilit…
Digital Quantum Simulations of the Non-Resonant Open Tavis-Cummings Model
Aidan N. Sims, Dhrumil Patel, Aby Philip +4
The open Tavis--Cummings model consists of quantum emitters interacting with a common cavity mode, accounts for losses and decoherence, and is frequently explored for quantum i…
Evolved Quantum Boltzmann Machines
Michele Minervini, Dhrumil Patel, Mark M. Wilde
We introduce evolved quantum Boltzmann machines as a variational ansatz for quantum optimization and learning tasks. Given two parameterized Hamiltonians and , an evol…
Sample-based Hamiltonian and Lindbladian simulation: Non-asymptotic analysis of sample complexity
Byeongseon Go, Hyukjoon Kwon, Siheon Park +2
Density matrix exponentiation (DME) is a quantum algorithm that processes multiple copies of a program state to realize the Hamiltonian evolution . Wave matrix Lindb…
Natural gradient and parameter estimation for quantum Boltzmann machines
Dhrumil Patel, Mark M. Wilde
Thermal states play a fundamental role in various areas of physics, and they are becoming increasingly important in quantum information science, with applications related to semi-d…
Quantum Boltzmann machine learning of ground-state energies
Dhrumil Patel, Daniel Koch, Saahil Patel +1
Estimating the ground-state energy of Hamiltonians is a fundamental task for which it is believed that quantum computers can be helpful. Several approaches have been proposed towar…
QSlack: A slack-variable approach for variational quantum semi-definite programming
Jingxuan Chen, Hanna Westerheim, Zoë Holmes +6
Solving optimization problems is a key task for which quantum computers could possibly provide a speedup over the best known classical algorithms. Particular classes of optimizatio…
Dual-VQE: A quantum algorithm to lower bound the ground-state energy
Hanna Westerheim, Jingxuan Chen, Zoë Holmes +6
The variational quantum eigensolver (VQE) is a hybrid quantum-classical variational algorithm that produces an upper-bound estimate of the ground-state energy of a Hamiltonian. As…